Training a model with dynamic resolutions of 256\(\sim \) 512 reduces the training speed with frequent GPU memory swaps. Therefore, we train the model with regular resolutions, i.e., resizing images from 512 to 256 and then back to 512. For Indoor, there is one cycle for each epoch. For Places2, there are 64 cycles for each epoch. Such a local monotonic resizing makes the training smooth without missing diversity. And the dynamic resolution based training can effectively save the training cost compared with the training with a full 512 image size. Moreover, it benefits to learn relative position encoding for our proposed MPE as discussed in [1]}.
